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Open AccessArticle

Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN

1
Center for Hydrometeorology and Remote Sensing (CHRS), The Henry Samueli School of Engineering, Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697, USA
2
Department of Computer Sciences, University of California, Irvine, CA 92697, USA
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Department of Earth System Science, University of California, Irvine, CA 92697, USA
4
Center for Climate Sciences, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
5
NASA Advanced Supercomputing Division/NASA Ames Research Center Moffet Field, Mountain View, CA 94035, USA
6
Bay Area Environmental Research Institute/NASA Ames Research Center, Moffett Field, CA 94035, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2193; https://doi.org/10.3390/rs11192193
Received: 2 August 2019 / Revised: 3 September 2019 / Accepted: 17 September 2019 / Published: 20 September 2019
(This article belongs to the Special Issue Earth Monitoring from A New Generation of Geostationary Satellites)
In this paper, we present a state-of-the-art precipitation estimation framework which leverages advances in satellite remote sensing as well as Deep Learning (DL). The framework takes advantage of the improvements in spatial, spectral and temporal resolutions of the Advanced Baseline Imager (ABI) onboard the GOES-16 platform along with elevation information to improve the precipitation estimates. The procedure begins by first deriving a Rain/No Rain (R/NR) binary mask through classification of the pixels and then applying regression to estimate the amount of rainfall for rainy pixels. A Fully Convolutional Network is used as a regressor to predict precipitation estimates. The network is trained using the non-saturating conditional Generative Adversarial Network (cGAN) and Mean Squared Error (MSE) loss terms to generate results that better learn the complex distribution of precipitation in the observed data. Common verification metrics such as Probability Of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), Bias, Correlation and MSE are used to evaluate the accuracy of both R/NR classification and real-valued precipitation estimates. Statistics and visualizations of the evaluation measures show improvements in the precipitation retrieval accuracy in the proposed framework compared to the baseline models trained using conventional MSE loss terms. This framework is proposed as an augmentation for PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network- Cloud Classification System) algorithm for estimating global precipitation. View Full-Text
Keywords: precipitation; multispectral satellite imagery; machine learning; convolutional neural networks (CNNs); generative adversarial networks (GANs) precipitation; multispectral satellite imagery; machine learning; convolutional neural networks (CNNs); generative adversarial networks (GANs)
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MDPI and ACS Style

Hayatbini, N.; Kong, B.; Hsu, K.-L.; Nguyen, P.; Sorooshian, S.; Stephens, G.; Fowlkes, C.; Nemani, R.; Ganguly, S. Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN. Remote Sens. 2019, 11, 2193.

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